Distributed Triggers for Peer Data Management

  • Verena Kantere
  • Iluju Kiringa
  • Qingqing Zhou
  • John Mylopoulos
  • Greg McArthur
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4275)


A network of peer database management systems differs from conventional multidatabase systems by assuming absence of any central control, no global schema, transient connection of participating peer DBMSs, and evolving coordination among databases. We describe distributed triggers to support data coordination in this setting. The execution of our triggers requires coordination among the involved peer databases. We present an SQL3 compatible trigger language for the P2P setting. We also extend the SQL3 processing mechanism to this setting. Our trigger processing mechanism consists of an execution semantics, a set of termination protocols to deal with peer transiency, and a set of protocols for managing peer acquaintances in presence of distributed triggers. We show preliminary experimental results about our mechanism.


Mapping Table Database Operation Condition Evaluator Action Executor Execution Semantic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison Wesley, Reading (1995)MATHGoogle Scholar
  2. 2.
    Arenas, M., Kantere, V., Kementsietsidis, A., Kiringa, I., Miller, R.J., Mylopoulos, J.: The hyperion project: from data integration to data coordination. SIGMOD Record 32(3), 53–58 (2003)CrossRefGoogle Scholar
  3. 3.
    Arizio, R., Bomitali, B., Demarie, M.L., Limongiello, A., Mussa. P.L.: Managing inter-database dependencies with rules + quasi-transactions. In: Third International Workshop on Research Issues in Data Engineering: Interoperability in Multidatabase Systems, Vienna, April 1993, pp. 34–41 (1993)Google Scholar
  4. 4.
    Cochrane, R., Pirahesh, H., Mattos, N.: Integrating triggers and declarative constraints in sql database systems. In: VLDB, pp. 567–578 (1996)Google Scholar
  5. 5.
    Codd, E.F.: A relational model of data for large shared data banks. Communications of the ACM 13(6), 377–387 (1970)MATHCrossRefGoogle Scholar
  6. 6.
    Elmagarmid, A., Rusinkiewicz, M., Sheth, A.: Management of Heterogeneous and Autonomous Database Systems. Morgan Kaufmann Publishers, San Francisco (1999)Google Scholar
  7. 7.
    Gradecki, J., Gradecki, J.: Mastering JXTA: Building Java Peer-to-Peer Applications. Wiley, Chichester (2002)Google Scholar
  8. 8.
    Kantere, V.: A rule mechanism for p2p data management. Technical report, University of Toronto, CSRG-469 (2003)Google Scholar
  9. 9.
    Kantere, V., Kiringa, I., Mylopoulos, J., Kementsietsidis, A., Arenas, M.: Coordinating peer databases using ECA rules. In: DBISP2P (2003)Google Scholar
  10. 10.
    Kantere, V., Mylopoulos, J., Kiringa, I.: A Distributed Rule Mechanism for Multidatabase Systems. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) CoopIS 2003, DOA 2003, and ODBASE 2003. LNCS, vol. 2888, pp. 56–73. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Kementsietsidis, A., Arenas, M., Miller, R.J.: Data mapping in peer-to-peer systems: Semantics and algorithmic issues. In: Sigmod (2003)Google Scholar
  12. 12.
    Kulkarni, K., Mattos, N., Cochrane, R.: Active database features in sql-3. In: Paton, N. (ed.) Active Rules in Database Systems, 1999, pp. 197–219. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  13. 13.
    Ooi, B.C., Shu, Y., Tan, K.-L.: Relational data sharing in peer-based data management systems. SIGMOD Record 32(3), 59–64 (2003)CrossRefGoogle Scholar
  14. 14.
    Özsu, M.T., Valduriez, P.: Principles of Distributed Database Systems, 2nd edn. Prentice Hall, Upper Saddle River (1999)Google Scholar
  15. 15.
    Sheth, A., Rusinkiewicz, M.: Management of Interdependent Data: Specifying Dependency and Consistency Requirements. In: Proc. of the Workshop on the Management of Replicated Data, Houston, TX (November 1990)Google Scholar
  16. 16.
    Sheth, A.P., Larson, J.A.: Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases. ACM Computing Surveys 22(3), 183–236 (1990)CrossRefGoogle Scholar
  17. 17.
    Tatarinov, I., Ives, Z.G., Madhavan, J., Halevy, A.Y., Suciu, D., Dalvi, N.N., Dong, X., Kadiyska, Y., Miklau, G., Mork, P.: The Piazza peer data management project. SIGMOD Record 32(3), 47–52 (2003)CrossRefGoogle Scholar
  18. 18.
    Vargas-Solar, G., Collet, C., Ribeiro, H.G.: Active Services for Federated Databases. In: ACM Symposium on Applied computing, Como, Italy, pp. 356–360 (2000)Google Scholar
  19. 19.
    Widom, J., Finkelstein, F.: Set-oriented production rules in relational database systems. In: Garcia-Molina, H., Jagadish, H.V. (eds.) Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data, pp. 259–270 (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Verena Kantere
    • 1
  • Iluju Kiringa
    • 2
  • Qingqing Zhou
    • 3
  • John Mylopoulos
    • 3
  • Greg McArthur
    • 3
  1. 1.National Technical University of Athens
  2. 2.University of Ottawa
  3. 3.University of Toronto

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